Source code for colour.colorimetry.transformations

# -*- coding: utf-8 -*-
"""
Colour Matching Functions Transformations
=========================================

Defines various educational objects for colour matching functions
transformations:

-   :func:`colour.colorimetry.RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs`
-   :func:`colour.colorimetry.RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs`
-   :func:`colour.colorimetry.RGB_10_degree_cmfs_to_LMS_10_degree_cmfs`
-   :func:`colour.colorimetry.LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs`
-   :func:`colour.colorimetry.LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs`

References
----------
-   :cite:`CIETC1-362006a` : CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental
    Chromaticity Diagram with Physiological Axes - Part 1. Commission
    Internationale de l'Eclairage. ISBN:978-3-901906-46-6
-   :cite:`CVRLp` : CVRL. (n.d.). CIE (2012) 10-deg XYZ
    "physiologically-relevant" colour matching functions. Retrieved June 25,
    2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz10.htm
-   :cite:`CVRLv` : CVRL. (n.d.). CIE (2012) 2-deg XYZ
    "physiologically-relevant" colour matching functions. Retrieved June 25,
    2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz2.htm
-   :cite:`Wyszecki2000be` : Wyszecki, Gùˆnther, & Stiles, W. S. (2000). The
    CIE 1964 Standard Observer. In Color Science: Concepts and Methods,
    Quantitative Data and Formulae (p. 141). Wiley. ISBN:978-0-471-39918-6
-   :cite:`Wyszecki2000bg` : Wyszecki, Gùˆnther, & Stiles, W. S. (2000). Table
    1(3.3.3). In Color Science: Concepts and Methods, Quantitative Data and
    Formulae (pp. 138-139). Wiley. ISBN:978-0-471-39918-6
"""

from __future__ import division, unicode_literals

import numpy as np

from colour.colorimetry import MSDS_CMFS_LMS, MSDS_CMFS_RGB, SDS_LEFS_PHOTOPIC
from colour.utilities import dot_vector, tstack

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2020 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-developers@colour-science.org'
__status__ = 'Production'

__all__ = [
    'RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs',
    'RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs',
    'RGB_10_degree_cmfs_to_LMS_10_degree_cmfs',
    'LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs',
    'LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs'
]


[docs]def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength): """ Converts *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions into the *CIE 1931 2 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength : numeric or array_like Wavelength :math:`\\lambda` in nm. Returns ------- ndarray *CIE 1931 2 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 1931 2 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`Wyszecki2000bg` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0113577..., 0.004102 , 0. ]) """ cmfs = MSDS_CMFS_RGB['Wright & Guild 1931 2 Degree RGB CMFs'] rgb_bar = cmfs[wavelength] rgb = rgb_bar / np.sum(rgb_bar) M1 = np.array([ [0.49000, 0.31000, 0.20000], [0.17697, 0.81240, 0.01063], [0.00000, 0.01000, 0.99000], ]) M2 = np.array([ [0.66697, 1.13240, 1.20063], [0.66697, 1.13240, 1.20063], [0.66697, 1.13240, 1.20063], ]) xyz = dot_vector(M1, rgb) xyz /= dot_vector(M2, rgb) x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2] V = SDS_LEFS_PHOTOPIC['CIE 1924 Photopic Standard Observer'].copy() V.align(cmfs.shape) L = V[wavelength] x_bar = x / y * L y_bar = L z_bar = z / y * L xyz_bar = tstack([x_bar, y_bar, z_bar]) return xyz_bar
[docs]def RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength): """ Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching functions into the *CIE 1964 10 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength : numeric or array_like Wavelength :math:`\\lambda` in nm. Returns ------- ndarray *CIE 1964 10 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 1964 10 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`Wyszecki2000be` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0096432..., 0.0037526..., -0.0000041...]) """ cmfs = MSDS_CMFS_RGB['Stiles & Burch 1959 10 Degree RGB CMFs'] rgb_bar = cmfs[wavelength] M = np.array([ [0.341080, 0.189145, 0.387529], [0.139058, 0.837460, 0.073316], [0.000000, 0.039553, 2.026200], ]) xyz_bar = dot_vector(M, rgb_bar) return xyz_bar
[docs]def RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(wavelength): """ Converts *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching functions into the *Stockman & Sharpe 10 Degree Cone Fundamentals* spectral sensitivity functions. Parameters ---------- wavelength : numeric or array_like Wavelength :math:`\\lambda` in nm. Returns ------- ndarray *Stockman & Sharpe 10 Degree Cone Fundamentals* spectral tristimulus values. Notes ----- - Data for the *Stockman & Sharpe 10 Degree Cone Fundamentals* already exists, this definition is intended for educational purpose. References ---------- :cite:`CIETC1-362006a` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0052860..., 0.0003252..., 0. ]) """ cmfs = MSDS_CMFS_RGB['Stiles & Burch 1959 10 Degree RGB CMFs'] rgb_bar = cmfs[wavelength] M = np.array([ [0.1923252690, 0.749548882, 0.0675726702], [0.0192290085, 0.940908496, 0.113830196], [0.0000000000, 0.0105107859, 0.991427669], ]) lms_bar = dot_vector(M, rgb_bar) lms_bar[..., -1][np.asarray(np.asarray(wavelength) > 505)] = 0 return lms_bar
[docs]def LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength): """ Converts *Stockman & Sharpe 2 Degree Cone Fundamentals* colour matching functions into the *CIE 2012 2 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength : numeric or array_like Wavelength :math:`\\lambda` in nm. Returns ------- ndarray *CIE 2012 2 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 2012 2 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`CVRLv` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0109677..., 0.0041959..., 0. ]) """ cmfs = MSDS_CMFS_LMS['Stockman & Sharpe 2 Degree Cone Fundamentals'] lms_bar = cmfs[wavelength] M = np.array([ [1.94735469, -1.41445123, 0.36476327], [0.68990272, 0.34832189, 0.00000000], [0.00000000, 0.00000000, 1.93485343], ]) xyz_bar = dot_vector(M, lms_bar) return xyz_bar
[docs]def LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength): """ Converts *Stockman & Sharpe 10 Degree Cone Fundamentals* colour matching functions into the *CIE 2012 10 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength : numeric or array_like Wavelength :math:`\\lambda` in nm. Returns ------- ndarray *CIE 2012 10 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 2012 10 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`CVRLp` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0098162..., 0.0037761..., 0. ]) """ cmfs = MSDS_CMFS_LMS['Stockman & Sharpe 10 Degree Cone Fundamentals'] lms_bar = cmfs[wavelength] M = np.array([ [1.93986443, -1.34664359, 0.43044935], [0.69283932, 0.34967567, 0.00000000], [0.00000000, 0.00000000, 2.14687945], ]) xyz_bar = dot_vector(M, lms_bar) return xyz_bar